Aerial photographyAerial photography (or airborne imagery) is the taking of photographs from an aircraft or other airborne platforms. When taking motion pictures, it is also known as aerial videography. Platforms for aerial photography include fixed-wing aircraft, helicopters, unmanned aerial vehicles (UAVs or "drones"), balloons, blimps and dirigibles, rockets, pigeons, kites, or using action cameras while skydiving or wingsuiting. Handheld cameras may be manually operated by the photographer, while mounted cameras are usually remotely operated or triggered automatically.
Aerial photographic and satellite image interpretationAerial photographic and satellite image interpretation, or just image interpretation when in context, is the act of examining s, particularly airborne and , to identify objects and judging their significance. This is commonly used in military aerial reconnaissance, using photographs taken from reconnaissance aircraft and reconnaissance satellites. The principles of image interpretation have been developed empirically for more than 150 years.
Unmanned aerial vehicleAn unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without any human pilot, crew, or passengers on board. UAVs were originally developed through the twentieth century for military missions too "dull, dirty or dangerous" for humans, and by the twenty-first, they had become essential assets to most militaries. As control technologies improved and costs fell, their use expanded to many non-military applications.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Transformer (machine learning model)A transformer is a deep learning architecture that relies on the parallel multi-head attention mechanism. The modern transformer was proposed in the 2017 paper titled 'Attention Is All You Need' by Ashish Vaswani et al., Google Brain team. It is notable for requiring less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl, by virtue of the parallelized processing of input sequence.
Aerial reconnaissanceAerial reconnaissance is reconnaissance for a military or strategic purpose that is conducted using reconnaissance aircraft. The role of reconnaissance can fulfil a variety of requirements including artillery spotting, the collection of , and the observation of enemy maneuvers. Aerial photography#History and Imagery intelligence#History Espionage balloon After the French Revolution, the new rulers became interested in using the balloon to observe enemy manoeuvres and appointed scientist Charles Coutelle to conduct studies using the balloon L'Entreprenant, the first military reconnaissance aircraft.
Generative pre-trained transformerGenerative pre-trained transformers (GPT) are a type of large language model (LLM) and a prominent framework for generative artificial intelligence. The first GPT was introduced in 2018 by OpenAI. GPT models are artificial neural networks that are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to generate novel human-like content. As of 2023, most LLMs have these characteristics and are sometimes referred to broadly as GPTs.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Attention (machine learning)Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based large language models.
Satellite imagerySatellite images (also Earth observation imagery, spaceborne photography, or simply satellite photo) are s of Earth collected by imaging satellites operated by governments and businesses around the world. Satellite imaging companies sell images by licensing them to governments and businesses such as Apple Maps and Google Maps. The first images from space were taken on sub-orbital flights. The U.S-launched V-2 flight on October 24, 1946, took one image every 1.5 seconds.
